Architecture and Design Automation for Application-Specific Processors Philip Brisk Assistant Professor Dept. of Computer Science and Engineering University of California, Riverside IEEE 9 th International Conference on ASIC (ASICON) Xiamen, ChinaOctober 26, 2011

Acknowledgment The vast majority of slides in this presentation are taken from the Ph.D. Thesis of my friend and collaborator, Dr. Theo Kluter (Ph.D., EPFL, 2010)

Five Stage RISC Pipeline I$RFD$RF Fetch DecodeExecuteMemoryWrite-back

Application-Specific Custom Unit (ASCU) for Instruction Set Extensions (ISEs) I$RFD$RF Fetch DecodeExecuteMemoryWrite-back ASCU

Automatic ISE Identification I$RFD$RF Fetch DecodeExecuteMemoryWrite-back ASCU Compiler HW Synthesis Applications Assembly code with ISEs

Overview Architecture Compilation and Synthesis Conclusion

Overview Architecture – Custom ISE Logic – I/O Bandwidth – Local memories and coherence Compilation and Synthesis Conclusion

Example: Luminance Conversion in JPEG Compression 19 cycles in software 17-bit values Fixed-point

Custom Hardware Implementation One single-ported memory 4 – 5 cycles (3 loads, 1 arithmetic, 1 store) Speedup: 3.8x – 4.8x R, G, B, and Y Memories 1 cycle for everything Speedup: 19x

Custom ISE Logic RF has 2 read ports RF has 1 write port Architectural Limitations Load data from memory into RF RF I/O bandwidth Performance 7 cycles (3 loads, 2 ASCU, 1 store) Speedup: 3.1x

Overview Architecture – Custom ISE Logic – I/O Bandwidth – Local memories and coherence Compilation and Synthesis Conclusion

I/O Bandwidth Constraint AES Algorithm Single round 4 stages Best ISE 22 inputs 22 outputs [Verma, Brisk, and Ienne, CASES 2007 & TCAD 2010] RF I/O constraints Noticeable slowdown

Pipeline Forwarding [Jayaseelan et al., DAC 2006] 1 output I/O Bandwidth limitations Input bandwidth depends on number of pipeline stages Does not increase output bandwidth Complicates instruction scheduling

Register File Clustering 4 inputs 1 output [Karuri et al., ICCAD 2007] I/O Bandwidth limitations Input bandwidth depends on number clusters Does not increase output bandwidth Compiler must eliminate inter-cluster copies More clusters => more copies NP-Hard

Shadow Registers [Cong et al., FPGA 2005] 1 output I/O Bandwidth No limitation on input bandwidth Does not increase output bandwidth Increases ISA bitwidth

Overview Architecture – Custom ISE Logic – I/O Bandwidth – Local memories and coherence Compilation and Synthesis Conclusion

Architecturally Visible Storage DMA transfers data between memory and AVS Coherence problem between AVS and D$ [Biswas et al., DATE 2006, TCAD 2007]

Example: IDCT (from JPEG)

The Coherence Problem

Overview Architecture – Custom ISE Logic – I/O Bandwidth – Local memories and coherence Coherent and Speculative DMA Virtual Ways Way Stealing Compilation and Synthesis Conclusion

Coherent DMA

Speculative DMA Coherent DMA loads and evicts the array from AVS during each iteration Speculative DMA waits until the array is overwritten in AVS memory by other data, or if the data is read/written by the D$.

Virtual Ways

AVS vs. Traditional Cache

AVS and Cache Ways are Similar if AVS Memory has 1-input, 1-output

Way Stealing

No AVS memories (reduced area) No coherence protocol

Coherent AVS Summary Speculative DMA – Requires a coherence protocol Lots of bus traffic Good solution for coherent multiprocessor systems – No limit on AVS memory organization – Uses standard cache IPs Virtual Ways – Requires non-traditional cache controller – No limit on AVS memory organization Way Stealing – Requires non-traditional cache – Number of ways limits number of AVS memories – All AVS memories have 1-input, 1-output – Keeps AVS memories within the cache

Overview Architecture Compilation and Synthesis – ISE Identification Algorithms Conclusion

SW and HW Costs

Convex and Non-Convex Cuts

Integrating AVS Memories

Single Cycle ISE Identification Problem Legality Constraints: – Convex cut – Contains no forbidden nodes – Number of inputs/outputs match architectural constraints (e.g., 2 RF inputs, 1 RF output) Objective: – Find the legal cut that maximizes speedup

Algorithms for ISE Identification Optimal (Exponential worst-case runtime) – Branch-and-bound search – Integer Linear Program Formulation Iterative Improvement – Evolutionary algorithms – Simulated annealing Polynomial-time Heuristics

Branch-and-Bound Search Example [Atasu et al., DAC 2003]

Branch-and-Bound Search Example [Atasu et al., DAC 2003]

ISE Identification Algorithms [Kastner et al., ICCAD 2001] [Brisk et al., CASES 2002] [Sun et al., ICCAD 2002] [Lee et al., ICCAD 2002] [Atasu et al., DAC 2003] [Goodwin and Petkov, DATE 2003] [Peymandoust et al., ASAP 2003] [Clark et al., MICRO 2003] [Sun et al., ICCAD 2003] [Lee et al., ISLPED 2003] [Cong et al., FPGA 2004] [Biswas et al., DAC 2004] [Yu and Mitra, DAC 2004] [Borin et al., ESTIMedia 2004] [Kastens et al., LCTES 2004] [Yu and Mitra, CASES 2004] [Pozzi and Ienne, CASES 2005] [Biswas et al., DAC 2005] [Atasu et al., CODES-ISSS 2005] [Sun et al., VLSI Design 2005] [Biswas et al., DATE 2006] [Galuzzi et al., CODES-ISSS 2006] [Sun et al., VLSI Design 2006] [Wong et al., HiPEAC 2007] [Verma et al., CASES 2007] [Pothineni et al., CDES 2007] Conferences [Atasu et al., IJPP 2003] [Clark et al., IJPP 2003] [Sun et al., TCAD 2004] [Clark et al., TCOMP 2005] [Pozzi et al., TCAD 2006] [Biswas et al., TVLSI 2006] [Sun et al., TCAD 2006] [Sun et al., TVLSI 2006] [Biswas et al., TCAD 2007] [Chen et al., TCAD 2007] [Sun et al., TCAD 2007] [Lee et al., TODAES 2007] [Bonzini and Pozzi, TVLSI 2008] [Zhao et al., IEICE Trans. Fund. 2008] [Atasu et al., TCAD 2008] [Murray et al., TECS 2009] [Verma et al., TCAD 2010] [Galuzzi and Bertels, TRETS 2011] Journals [Pothineni et al., VLSI Design 2007] [Bonzini and Pozzi, DATE 2007] [Atasu et al., DATE 2007] [Noori et al., DATE 2007] [Galuzzi et al., SAMOS 2007] [Galuzzi et al., ARC 2007] [Bonzini and Pozzi, ASAP 2007] [Wolinski and Kuchcinski, ASAP 2007] [Galuzzi et al., ARC 2007] [Yu and Mitra, FPL 2007] [Bennet et al., LCTES 2007] [Verma et al., ASPDAC 2008] [Wolinski and Kuchcinski, DATE 2008] [Galuzzi and Bertels, ARC 2008] [Atasu et al., ASAP 2008] [Galuzzi and Bertels, ReConFig 2008] [Pothineni et al., VLSI Design 2008] [Galuzzi et al., DATE 2009] [Martin et al., ASAP 2009] [Martin et al., SAMOS 2009] [Kamal et al., ASAP 2010] [Pothineni et al., VLSI Design 2010] [Ahn et al., ASPDAC 2011] [Xiao and Casseau, GLS-VLSI 2011] [Xiao and Casseau, ASAP 2011] [Ahn et al., CODES-ISSS 2011]

Overview Architecture Compilation and Synthesis Conclusion – Summary and Future Research Directions

Conclusion ASIP Architecture – Supply data bandwidth to ASCU – Ensuring coherence when using local memories ISE Identification – Problem formulation is well-understood – Extensions needed to support memory operations – Many effective algorithms exist

Future ASIP Research Directions Parallel and Multi-core ASIPs – Balance ISE speedup across many threads – ISE identification for parallel models of computation Concurrent state machines Synchronous Data Flow / Kahn Process Networks MapReduce Identify ACSU for Current AND Future Applications – Some ISEs are not known at design time – Must insert generality or programmability into the ACSU Application-specific GPUs – Identify vectorized and threaded ISEs – ACSU by hundreds of near-identical threads concurrently